A bandwidth efficient coding scheme for the Hubble Space Telescope

As a demonstration of the performance capabilities of trellis codes using multidimensional signal sets, a Viterbi decoder was designed. The choice of code was based on two factors. The first factor was its application as a possible replacement for the coding scheme currently used on the Hubble Space Telescope (HST). The HST at present uses the rate 1/3 nu = 6 (with 2 (exp nu) = 64 states) convolutional code with Binary Phase Shift Keying (BPSK) modulation. With the modulator restricted to a 3 Msym/s, this implies a data rate of only 1 Mbit/s, since the bandwidth efficiency K = 1/3 bit/sym. This is a very bandwidth inefficient scheme, although the system has the advantage of simplicity and large coding gain. The basic requirement from NASA was for a scheme that has as large a K as possible. Since a satellite channel was being used, 8PSK modulation was selected. This allows a K of between 2 and 3 bit/sym. The next influencing factor was INTELSAT's intention of transmitting the SONET 155.52 Mbit/s standard data rate over the 72 MHz transponders on its satellites. This requires a bandwidth efficiency of around 2.5 bit/sym. A Reed-Solomon block code is used as an outer code to give very low bit error rates (BER). A 16 state rate 5/6, 2.5 bit/sym, 4D-8PSK trellis code was selected. This code has reasonable complexity and has a coding gain of 4.8 dB compared to uncoded 8PSK (2). This trellis code also has the advantage that it is 45 deg rotationally invariant. This means that the decoder needs only to synchronize to one of the two naturally mapped 8PSK signals in the signal set

A hybrid M-algorithm/sequential decoder for convolutional and trellis codes

The Viterbi Algorithm (VA) is optimum in the sense of being maximum likelihood for decoding codes with a trellis structure. However, since the VA is in fact an exhaustive search of the code trellis, the complexity of the VA grows exponentially with the constraint length upsilon. This limits its application to codes with small values of upsilon and relatively modest coding gains. The M-Algorithm (MA) is a limited search scheme which carries forward M paths in the trellis, all of the same length. All successors of the M paths are extended at the next trellis depth, and all but the best M of these are dropped. Since a limited search convolutional decoder will flounder indefinitely if one of the paths in storage is not the correct one, the data are usually transmitted in blocks. It has been shown that the performance of the MA approaches the VA at high signal to noise ratios (SNR's) with an M which is far less than the 2 sup upsilon states in the full trellis. Thus the MA can be used with larger values of upsilon, making larger coding gains possible at high SNR's. However, it still requires a relatively large fixed computational effort to achieve good performance

A new SISO algorithm with application to turbo equalization

In this paper we propose a new soft-input soft-output equalization algorithm, offering very good performance/complexity tradeoffs. It follows the structure of the BCJR algorithm, but dynamically constructs a simplified trellis during the forward recursion. In each trellis section, only the M states with the strongest forward metric are preserved, similar to the M-BCJR algorithm. Unlike the M-BCJR, however, the remaining states are not deleted, but rather merged into the surviving states. The new algorithm compares favorably with the reduced-state BCJR algorithm, offering better performance and more flexibility, particularly for systems with higher order modulations.Comment: 5 pages, 7 figures, submitted to 2005 IEEE International Symposium on Information Theor

Spatially Coupled LDPC Codes Constructed from Protographs

In this paper, we construct protograph-based spatially coupled low-density parity-check (SC-LDPC) codes by coupling together a series of L disjoint, or uncoupled, LDPC code Tanner graphs into a single coupled chain. By varying L, we obtain a flexible family of code ensembles with varying rates and frame lengths that can share the same encoding and decoding architecture for arbitrary L. We demonstrate that the resulting codes combine the best features of optimized irregular and regular codes in one design: capacity approaching iterative belief propagation (BP) decoding thresholds and linear growth of minimum distance with block length. In particular, we show that, for sufficiently large L, the BP thresholds on both the binary erasure channel (BEC) and the binary-input additive white Gaussian noise channel (AWGNC) saturate to a particular value significantly better than the BP decoding threshold and numerically indistinguishable from the optimal maximum a-posteriori (MAP) decoding threshold of the uncoupled LDPC code. When all variable nodes in the coupled chain have degree greater than two, asymptotically the error probability converges at least doubly exponentially with decoding iterations and we obtain sequences of asymptotically good LDPC codes with fast convergence rates and BP thresholds close to the Shannon limit. Further, the gap to capacity decreases as the density of the graph increases, opening up a new way to construct capacity achieving codes on memoryless binary-input symmetric-output (MBS) channels with low-complexity BP decoding.Comment: Submitted to the IEEE Transactions on Information Theor

On the Minimum Distance of Generalized Spatially Coupled LDPC Codes

Families of generalized spatially-coupled low-density parity-check (GSC-LDPC) code ensembles can be formed by terminating protograph-based generalized LDPC convolutional (GLDPCC) codes. It has previously been shown that ensembles of GSC-LDPC codes constructed from a protograph have better iterative decoding thresholds than their block code counterparts, and that, for large termination lengths, their thresholds coincide with the maximum a-posteriori (MAP) decoding threshold of the underlying generalized LDPC block code ensemble. Here we show that, in addition to their excellent iterative decoding thresholds, ensembles of GSC-LDPC codes are asymptotically good and have large minimum distance growth rates.Comment: Submitted to the IEEE International Symposium on Information Theory 201

New Codes on Graphs Constructed by Connecting Spatially Coupled Chains

A novel code construction based on spatially coupled low-density parity-check (SC-LDPC) codes is presented. The proposed code ensembles are described by protographs, comprised of several protograph-based chains characterizing individual SC-LDPC codes. We demonstrate that code ensembles obtained by connecting appropriately chosen SC-LDPC code chains at specific points have improved iterative decoding thresholds compared to those of single SC-LDPC coupled chains. In addition, it is shown that the improved decoding properties of the connected ensembles result in reduced decoding complexity required to achieve a specific bit error probability. The constructed ensembles are also asymptotically good, in the sense that the minimum distance grows linearly with the block length. Finally, we show that the improved asymptotic properties of the connected chain ensembles also translate into improved finite length performance.Comment: Submitted to IEEE Transactions on Information Theor

Deriving Good LDPC Convolutional Codes from LDPC Block Codes

Low-density parity-check (LDPC) convolutional codes are capable of achieving excellent performance with low encoding and decoding complexity. In this paper we discuss several graph-cover-based methods for deriving families of time-invariant and time-varying LDPC convolutional codes from LDPC block codes and show how earlier proposed LDPC convolutional code constructions can be presented within this framework. Some of the constructed convolutional codes significantly outperform the underlying LDPC block codes. We investigate some possible reasons for this "convolutional gain," and we also discuss the --- mostly moderate --- decoder cost increase that is incurred by going from LDPC block to LDPC convolutional codes.Comment: Submitted to IEEE Transactions on Information Theory, April 2010; revised August 2010, revised November 2010 (essentially final version). (Besides many small changes, the first and second revised versions contain corrected entries in Tables I and II.